Adjoining Pixels Research Articles

Memory Organization for Image Segmentation Based on Region Growing

A solution is proposed for determining and organizing the size of the FIFO-stack in memory, required for load the coordinates of adjoining pixels in an image segmentation algorithm based on region growing. The FIFO-stack, organized on the idea of a ring multi-bit shift register, is considered, and the expression of memory calculations is performed through storing process in parallel registers. The conditions for maximum loading of the memory stack are formulated, for which an expression is obtained that allowing to accurately determining the required size of the FIFO-stack, which provides memory resources savings.

Machine Learning Model based Image and Video Colorization

In this paper, we present an intuitive framework for clients to effectively colorize the regular pictures of complex scenes. In our framework, colorization system is expressly isolated into two phases: Shading naming and Shading planning. Pixels that ought to generally have comparative tones are assembled into lucid districts in the shading naming stage, and the shading planning stage is then acquainted with additional tweak the tones in each intelligible locale. To deal with textures ordinarily found in normal pictures, we propose another shading marking plan that bunches not just adjoining pixels with comparative force yet additionally distant pixels with comparable surface. Propelled by the knowledge into the complementary nature moved by the exceptionally contrastive areas and the smooth areas, we utilize a perfection guide to direct the joining of power progression and surface comparability limitations in the plan of our labeling calculation. Inside each sound area acquired from the shading marking stage, the shading planning is applied to produce distinctive colorization impact by allotting shadings to a couple of pixels in the district. A bunch of natural interface apparatuses is intended for marking, shading and changing the outcome. We show convincing consequences of colorizing assuming measure of client input.

RETRACTED: Design and implementation of parallel algorithm for image matching based on Hausdorff Distance

Object matching two-dimensional images in computer vision has become a significant subject of article acknowledgment and picture investigation. Hausdorff Distance assumes a significant function in coordinating image. To proposed system parallel algorithm for image matching manage the instance of arbitrary commotion, image coordinating, new Hausdorff Distance is proposed in this. In contrast to coordinating two twofold pictures, different techniques, the proposed strategy might be coordinated with a few dark scale image pixel esteems. One case of article acknowledgment is utilized to show the productivity of the proposed strategy. The outcomes demonstrated that, contrasted and, the new Hausdorff Distance (HD) might be more alluring approach to discard image clamor coordinating, because of the extensive reflection to decide the dark scale data Hausdorff Distance of adjoining pixels in the shooting of his realities into account. Besides, the strategy can be acknowledged in a straightforward manner.

Hyperspectral Anomaly Detection With Morphological Random Walker

This work proposes a new morphological random walker (MRW) method for hyperspectral anomaly detection. The proposed method introduces a morphology-based objective function into a random walker (RW) algorithm, sufficiently exploiting spatial morphological property and spatial similarity of HSIs for detection. The MRW method comprises two major stages. Firstly, we employ the extended morphological profiles (EMPs) and different operations to extract the spatial morphological property of HSIs. Second, according to the morphological property, we construct a morphology-based objective function. This function is incorporated into the RW-based optimization model, encoding the spatial similarity of HSIs in a weighted graph. Two factors determine the class of test pixels, including the spatial morphological information learned by EMPs, and the spatial correlation among adjoining pixels modeled by the weighted graph. Since the two factors are well considered in the MRW method, the proposed method illustrates outstanding detection performances for several widely used real HSIs.

A Novel Gray Image Enhancement Using the Regional Similarity Transformation Function and Dragonfly Algorithm

Image enhancement is a necessary and indispensable technique for increasing the quality of digital images. The main task is to generate a new intensity value for each pixel in the image using a transformation function after the input image receives the intensity value of each pixel. The proposed transfer function in this study is called the Regional Similarity Transfer Function (RSTF) that considers the density distribution similarity between adjoining pixels. Dragonfly Algorithm (DA) intuitive optimization technique, which is preferred in engineering applications, has been used to optimize the parameter values of the proposed transfer function. Image quality evaluation is performed with six criteria between the improved and original images. Our experimental results show that the intensity distribution between adjoining pixels show an increase in contrast and brightness over the similarity degree. Excessive brightness, blur, and deterioration in the images is resolved with the proposed method.

Removal of random valued impulse noise from grayscale images using quadrant based spatially adaptive fuzzy filter

An efficient image restoration technique based on spatially linked-directional adjoining pixels and fuzzy logic for addressing moderate and highly corrupted grayscale images with Random Valued Impulse Noise is presented. The proposed technique decomposes a larger sized impulsive region of an image into numerous overlapping small patches for low as well as high density impulse noise estimation with enhanced image restoration results. This subdivision highlights the details (edges, lines and textures) present in the neighborhood and the locations of impulsive pixels are relocated in multiple regions. Direction-based fuzzy rules give appropriate reasoning for edge and texture detection in an image. A switching technique based fuzzified degree identifies a certain pixel of an image as a noise-free, noisy or edge pixel in the filtering phase. Instead of using a static threshold, a directional non-parametric approach is introduced that determines and sets the threshold adaptively which empowers the proposed filter to handle different types of images automatically. Extensive simulations are conducted on a large set of monochrome images contaminated with varying degree of impulse noise densities. Objective analysis performed using popular quantitative measures and subjective evaluation of the results shows the efficacy of the proposed filter over most of the bench-marked denoising filters.

Segmentation of AFM images based on wave regions growing of local maximums with pixels selection in the decrease order of values

An algorithm for segmentation of images of atomic force microscopy is developed by using wave-growing regions around local maxima as a result of adjoining neighboring pixels to them, selected in descending order of values. The essence of the algorithm is to use the brightness threshold, gradually changing from maximum to minimum, to select growth points or to join existing areas. The features of the developed segmentation algorithm are iteratively expandable boundaries, the choice of initial growth points and points attached to regions with a focus on threshold values with a gradual decrease from maximum to minimum. These features made it possible to eliminate the segmentation errors of the AFM images, characteristic of the algorithms of the marker watershed, the cultivation of areas and the watershed using the classical Vincent–Sollie algorithm, which are commonly used. The developed algorithm was compared with standard algorithms such as: classic watershed algorithm, marker watershed, growing areas. The comparison was carried out on test and original AFM images. The algorithms were implemented in Matlab and C ++. A set of binary masks was used to quantify segmentation errors. The results showed that the developed algorithm provides the selection of the boundaries of the regions without errors and a higher segmentation rate in comparison with the algorithms of growing the regions and the Vincent–Sollie watershed. The result can be used to process AFM images of the surfaces of inorganic materials in the submicro and nano range.

Local thresholding of degraded or unevenly illuminated documents using fuzzy inclusion and entropy measures

There are applications in which the content of a scanned document needs to be recognized or improved. We often achieve this by converting our input into a binary image and this is in fact the first step in many document analysis systems or optical character recognition (OCR) processes. In cases where our input is degraded or has a non-uniform illumination, global thresholding algorithms fail to deliver adequate results. For this reason, we have to use some local thresholding techniques which binarize each pixel based on the grayscale information of its adjoining pixels. In this paper, we present a local thresholding method based on specific fuzzy inclusion and entropy measures which we introduced in some of our previous work. We use these indicators to measure specific attributes of the neighborhood of a pixel and then, based on these values, an appropriate threshold is calculated. We don’t use the histogram of the image or any statistical measures and contrast parameters depending on the input. It is an open, automated and adaptable procedure and in this presentation we see some implementations of a more general algorithm along with some specific results. Our main domain of experimentation consists of texts containing lighting “irregularities” but some remarks regarding further generalization are being made as well. We also comment on other potential of these measures and the prospect of being connected with other studies that already use fuzzy inclusion and entropy measures.

REVISITING INTRINSIC CURVES FOR EFFICIENT DENSE STEREO MATCHING

Abstract. Dense stereo matching is one of the fundamental and active areas of photogrammetry. The increasing image resolution of digital cameras as well as the growing interest in unconventional imaging, e.g. unmanned aerial imagery, has exposed stereo image pairs to serious occlusion, noise and matching ambiguity. This has also resulted in an increase in the range of disparity values that should be considered for matching. Therefore, conventional methods of dense matching need to be revised to achieve higher levels of efficiency and accuracy. In this paper, we present an algorithm that uses the concepts of intrinsic curves to propose sparse disparity hypotheses for each pixel. Then, the hypotheses are propagated to adjoining pixels by label-set enlargement based on the proximity in the space of intrinsic curves. The same concepts are applied to model occlusions explicitly via a regularization term in the energy function. Finally, a global optimization stage is performed using belief-propagation to assign one of the disparity hypotheses to each pixel. By searching only through a small fraction of the whole disparity search space and handling occlusions and ambiguities, the proposed framework could achieve high levels of accuracy and efficiency.

REVISITING INTRINSIC CURVES FOR EFFICIENT DENSE STEREO MATCHING

Dense stereo matching is one of the fundamental and active areas of photogrammetry. The increasing image resolution of digital cameras as well as the growing interest in unconventional imaging, e.g. unmanned aerial imagery, has exposed stereo image pairs to serious occlusion, noise and matching ambiguity. This has also resulted in an increase in the range of disparity values that should be considered for matching. Therefore, conventional methods of dense matching need to be revised to achieve higher levels of efficiency and accuracy. In this paper, we present an algorithm that uses the concepts of intrinsic curves to propose sparse disparity hypotheses for each pixel. Then, the hypotheses are propagated to adjoining pixels by label-set enlargement based on the proximity in the space of intrinsic curves. The same concepts are applied to model occlusions explicitly via a regularization term in the energy function. Finally, a global optimization stage is performed using belief-propagation to assign one of the disparity hypotheses to each pixel. By searching only through a small fraction of the whole disparity search space and handling occlusions and ambiguities, the proposed framework could achieve high levels of accuracy and efficiency.

Estimate of the neutron fields in ATLAS based on ATLAS-MPX detectors data

The ATLAS-MPX detectors are based on Medipix2 silicon devices designed by CERN for the detection of different types of radiation. These detectors are covered with converting layers of 6LiF and polyethylene (PE) to increase their sensitivity to thermal and fast neutrons, respectively. These devices allow the measurement of the composition and spectroscopic characteristics of the radiation field in ATLAS, particularly of neutrons. These detectors can operate in low or high preset energy threshold mode. The signature of particles interacting in a ATLAS-MPX detector at low threshold are clusters of adjacent pixels with different size and form depending on their type, energy and incidence angle. The classification of particles into different categories can be done using the geometrical parameters of these clusters. The Medipix analysis framework (MAFalda) — based on the ROOT application — allows the recognition of particle tracks left in ATLAS-MPX devices located at various positions in the ATLAS detector and cavern. The pattern recognition obtained from the application of MAFalda was configured to distinguish the response of neutrons from other radiation. The neutron response at low threshold is characterized by clusters of adjoining pixels (heavy tracks and heavy blobs) left by protons and heavy ions resulting from neutron interactions in the converting layers of the ATLAS-MPX devices. The neutron detection efficiency of ATLAS-MPX devices has been determined by the exposure of two detectors of reference to radionuclide sources of neutrons (252Cf and 241AmBe). With these results, an estimate of the neutrons fields produced at the devices locations during ATLAS operation was done.

Study on fabrication of 3-D microstructures by synchrotron radiation based on pixels exposed lithography

We report research on the development of a method of fabricating three-dimensional microstructures that uses synchrotron radiation light. Some research on three-dimensional processing methods using SR lithography has already been reported. They have involved techniques of applying exposure energy distributions to resist surfaces. Complicated energy distributions need to be applied to resist surfaces to fabricate arbitrary three-dimensional structures. However, we devised a new method that made it possible to fabricate arbitrary three-dimensional microstructures by using a mask with pattern created function. The advantages of this method are that it is suitable for rapid prototyping and it reduces the fabrication time and cost since it is not necessary to fabricate conventional photolithographic masks. Our research involved a basic experiment on this method of fabrication where we succeeded in fabricating a free-form surface by exposing it through an overlapping array of pixels that created a single aperture. Moreover, the pixel size could be made smaller than the aperture size by overlapping adjoining pixels.

Detection of <inline-formula><math display="inline" overflow="scroll"><mrow><mi>LSB</mi><mo>±</mo><mn>1</mn></mrow></math></inline-formula> steganography based on co-occurrence matrix and bit plane clipping

Spatial LSB±1 steganography changes smooth characteristics between adjoining pixels of the raw image. We present a novel steganalysis method for LSB±1 steganography based on feature vectors derived from the co-occurrence matrix in the spatial domain. We investigate how LSB±1 steganography affects the bit planes of an image and show that it changes more least significant bit (LSB) planes of it. The co-occurrence matrix is derived from an image in which some of its most significant bit planes are clipped. By this preprocessing, in addition to reducing the dimensions of the feature vector, the effects of embedding were also preserved. We compute the co-occurrence matrix in different directions and with different dependency and use the elements of the resulting co-occurrence matrix as features. This method is sensitive to the data embedding process. We use a Fisher linear discrimination (FLD) classifier and test our algorithm on different databases and embedding rates. We compare our scheme with the current LSB±1 steganalysis methods. It is shown that the proposed scheme outperforms the state-of-the-art methods in detecting the LSB±1 steganographic method for grayscale images.

GIS-based modeling of secondary hydrocarbon migration pathways and its application in the northern Songliao Basin, northeast China

Hydrocarbon migration pathways are the linkage between hydrocarbon source areas and accumulation sites. Modeling accurately the pathways of hydrocarbon migration is of important significance in determining the location of favorable petroleum exploration targets. In this paper, GIS-based modeling algorithms are presented for searching the pathways of secondary hydrocarbon migration by considering the geologic mechanisms. These algorithms are constructed on a raster digital elevation model (DEM) of the top boundary of a carrier bed, in which secondary migration occurred. On the DEM, a 3×3 pixel sized window is used for searching. The center of the search window is initially assumed at a point on the boundary of a hydrocarbon source area, from which the secondary hydrocarbon migration starts. The altitude values in the eight adjoining pixels enclosing the central pixel of the search window are compared, and the pixel with an altitude value not only larger than the central pixel but also the largest of the eight surrounding pixels is the next target pixel where the search window will move to. Each searched path will terminate at either a convex point, which indicates the existence of a trap for hydrocarbon accumulation, or a point on the boundary of the DEM, which suggests that the pathway of hydrocarbon migration extends outside the DEM. In addition, a concourse point of two paths is also considered as a termination point of either path. The algorithms were successfully applied in the modeling of the secondary migration pathways in the northern Songliao Basin, northeast China. The modeled results matched well with drilling data, suggesting the robustness of the algorithms.

All-Optical Multifunctional Logic Gates for Image Information Using Photorefractive Two-Wave Mixing

In this paper, we propose all-optical multifunctional logic gates for image information using photorefractive (PR) two-wave mixing. The optical setup is simply configured compared with the other all-optical logic gates for image information. The XOR-, OR-, and AND-operation are all-optically performed in the same optical setup through the transitional response of the PR medium. One can switch these logic operations simply by means of the on-off control of the signal and the pump beam illumination. We analyze the spatial distribution of beam intensity in these logic gates using a finite-difference beam propagation method (FD-BPM) and the crosstalk between adjoining pixels is examined from the result. We also experimentally verify that the XOR-, OR-, and AND-gates are realized in the same optical setup.

Gradient energy detection of LSB steganography

The spatial Least Significant Bit (LSB) steganography results in the alteration of the smooth characteristics between adjoining pixels of the raw image. The relation between the length of embedded message and the gradient energy is theoretically analyzed, and then a steganalysis and detection method, named Gradient Energy-Flipping Rate (GEFR) detection is proposed. Based on the analysis of the variation of the gradient energy, which results from the LSB steganography in color and grayscale image, the secret message embedded in the target image is detected, and the length of the embedded message is estimated. The method is proved effective and accurate by simulation (detection rate reaches 0.01bit per pixel).